Chorus: Harmonizing Context and Sensing Signals for Data-Free Model Customization in IoT

ArXi:2512.15206v2 Announce Type: replace A key bottleneck toward scalable IoT sensing is how to efficiently adapt AI models to new deployment conditions. In real-world IoT systems, sensor data is collected under diverse contexts, such as sensor placements or ambient environments, which alter signal patterns and degrade downstream performance. Traditional domain adaptation and generalization methods often ignore such contextual information or incorporate it in overly simplistic ways, making them ineffective under unseen context shifts after deployment.